Titlebook: Metaheuristic Algorithms for Image Segmentation: Theory and Applications; Diego Oliva,Mohamed Abd Elaziz,Salvador Hinojosa Book 2019 Sprin

angina-pectoris

Image Segmentation Using Metaheuristics,to different regions which have the same properties such as color, texture, shape, and others. There are several image segmentation methods proposed to achieve this task, which include the traditional and other global methods. In this chapter, we present a review of the most popular three image segm

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使残废

,Otsu’s Between Class Variance and the Tree Seed Algorithm, tree seed algorithm (TSA) which emulates the tree generations. The proposed TSA used the maximum between class variance criterion (Otsu) as a fitness function. In order to evaluate the performance of the proposed method a set of images are used and the results and compared with other methods. The e

培养

,Image Segmentation Using Kapur’s Entropy and a Hybrid Optimization Algorithm,, the meta-heuristic algorithms are commonly used, they have the ability to find the global solution in a reduced number of iterations. Based on this concept, this chapter presents an improvement of the salp swarm algorithm based on artificial bee colony as an alternative image segmentation method.

nonchalance

Tsallis Entropy for Image Thresholding,hen it is applied to multilevel thresholding, its evaluation becomes computationally expensive, since each threshold point adds restrictions, multimodality and complexity to its functional formulation. Therefore, in the process of finding the appropriate threshold values, it is desired to limit the

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Isolate

Image Segmentation by Gaussian Mixture,mentation approaches in specific when are used thresholding mechanism. The alternative is to use methods that are able to manage uncertainties and ambiguities presented in the pixel’s classification. Fuzzy entropy methods then are interesting alternatives that permits to handle the situations descri

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同位素

Clustering Algorithms for Image Segmentation,natives for image segmentation. Such approaches consider the pixels as data in a multidimensional space and using different rules thy classify the information into different groups according to the features. This chapter presents the clustering algorithms that are commonly used for image segmentatio